An SVMD-based mode extraction criterion for geocentre motion analysis

SUMMARY The Geocentre Motion (GCM) time-series captures periodic variations arising from diverse Earth system changes. This study pioneers the use of Successive Variational Mode Decomposition (SVMD) in GCM research, enabling the precise extraction and analysis of these meaningful geophysical signals. SVMD outperformed Singular Spectrum Analysis (SSA) by effectively isolating signals and minimizing interference from components with similar variance contributions. However, a high maximum penalty factor in SVMD may lead to noise-dominated Intrinsic Mode Functions (IMFs). To overcome this limitation, we propose an extraction criterion that utilizes the standard deviation of the correlation coefficient and mean kurtosis as thresholds. Validations with simulations and the real GCM time-series demonstrate its superiority over traditional single- and dual-threshold criteria, effectively retaining valuable information while excluding most noise-dominated IMFs. This improved approach is further employed to explore the geophysical driving factors of key periodic variations in the GCM time-series, focusing on the annual, semi-annual, 10.5-yr, 451-d, ∼160-d and ∼120-d periods. Multisource GCM analyses combined with the fingerprint method reveal distinct contributions from the Antarctic and Greenland ice sheets, terrestrial water storage, continental glaciers and atmosphere–ocean interactions to different periodic signals. This study provides a robust methodology for decomposing GCM and attributing its variations to underlying Earth system changes, advancing our understanding and interpretation of global mass redistribution.